A Skeletal Parallel Framework with Fusion Optimizer for GPGPU Programming

Sato, Shigeyuki; Iwasaki, Hideya

doi:10.1007/978-3-642-10672-9_8

Cited by 27 publications

(23 citation statements)

References 24 publications

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“…Existing skeleton-based parallelization for GPUs uses classes such as map, reduce and map-reduce [7] [18]. To achieve a finer-grained classification, this work uses a domain specific classification, yielding high hardware efficiency.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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Skeleton-based automatic parallelization of image processing algorithms for GPUs

Nugteren

Corporaal

Mesman

2011

2011 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation

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Abstract-Graphics Processing Units (GPUs) are becoming increasingly important in high performance computing. To maintain high quality solutions, programmers have to efficiently parallelize and map their algorithms. This task is far from trivial, leading to the necessity to automate this process.In this paper, we present a technique to automatically parallelize and map sequential code on a GPU, without the need for code-annotations. This technique is based on skeletonization and is targeted at image processing algorithms. Skeletonization separates the structure of a parallel computation from the algorithm's functionality, enabling efficient implementations without requiring architecture knowledge from the programmer. We define a number of skeleton classes, each enabling GPU specific parallelization techniques and optimizations, including automatic thread creation, on-chip memory usage and memory coalescing.Recently, similar skeletonization techniques have been applied to GPUs. Our work uses domain specific skeletons and a finergrained classification of algorithms. Comparing skeleton-based parallelization to existing GPU code generators in general, we potentially achieve a higher hardware efficiency by enabling algorithm restructuring through skeletons.In a set of benchmarks, we show that the presented skeletonbased approach generates highly optimized code, achieving high data throughput. Additionally, we show that the automatically generated code performs close or equal to manually mapped and optimized code. We conclude that skeleton-based parallelization for GPUs is promising, but we do believe that future research must focus on the identification of a finer-grained and complete classification.

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Section: Discussion and Future Workmentioning

confidence: 99%

“…A related technique, based on patterns, has been presented for SIMD processors in general by Manniesing et al [13]. Recently, skeleton-based parallelization has been applied to GPUs for a limited amount of skeletons in [18] and [7]. Our work uses domain specific skeletons and a finer-grained classification.…”

Section: Related Workmentioning

confidence: 99%

Skeleton-based automatic parallelization of image processing algorithms for GPUs

Nugteren

Corporaal

Mesman

2011

2011 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation

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“…Sato and Iwasaki [30] describe a C++ library for GPGPU programming that includes a fusion mechanism based on list homomorphisms [25]. The fusion transformation itself is implemented as a source to source translation.…”

Section: Related Workmentioning

confidence: 99%

Optimising purely functional GPU programs

et al. 2013

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Purely functional, embedded array programs are a good match for SIMD hardware, such as GPUs. However, the naive compilation of such programs quickly leads to both code explosion and an excessive use of intermediate data structures. The resulting slowdown is not acceptable on target hardware that is usually chosen to achieve high performance.In this paper, we discuss two optimisation techniques, sharing recovery and array fusion, that tackle code explosion and eliminate superfluous intermediate structures. Both techniques are well known from other contexts, but they present unique challenges for an embedded language compiled for execution on a GPU. We present novel methods for implementing sharing recovery and array fusion, and demonstrate their effectiveness on a set of benchmarks.

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“…To date, numerous automatic CPU-to-GPU source parallelization translation tools [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], including algorithmic skeleton based [14][15][16], polyhedral model based [9][10][11][12][13], or directive based [17][18][19][20][21][22][23] have been developed for academic and commercial use. While their acceleration is promising, utilizing them by normal users in general real-word applications is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation

Yang

Dong

Codreanu

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-SMEs (Small and medium-sized enterprises), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck on the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPU (Graphics processing units) programming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and hard-to-use. Little attentions have been paid to the applicability, usability and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system, (GPSME) for inexperienced users to utilize GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with new kernel generation scheme and memory management hierarchy to optimize its performance. A web-service based interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with non-expert GPU users in 4 SMEs reflect that GPSME system can efficiently accelerate real-world applications with at least 4x and have a better applicability, usability and learnability than existing automatic CPU-to-GPU source translators.

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A Skeletal Parallel Framework with Fusion Optimizer for GPGPU Programming

Cited by 27 publications

References 24 publications

Skeleton-based automatic parallelization of image processing algorithms for GPUs

Skeleton-based automatic parallelization of image processing algorithms for GPUs

Optimising purely functional GPU programs

Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation

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